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Khalil, Mohammad; Prinsloo, Paul; Slade, Sharon – Journal of Computing in Higher Education, 2023
Since its inception in 2011, Learning Analytics has matured and expanded in terms of reach (e.g., primary and K-12 education) and in having access to a greater variety, volume and velocity of data (e.g. collecting and analyzing multimodal data). Its roots in multiple disciplines yield a range and richness of theoretical influences resulting in an…
Descriptors: Learning Theories, Learning Analytics, Literature Reviews, Interdisciplinary Approach
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Kaliisa, Rogers; Jivet, Ioana; Prinsloo, Paul – International Journal of Educational Technology in Higher Education, 2023
Higher education institutions are moving to design and implement teacher-facing learning analytics (LA) dashboards with the hope that instructors can extract deep insights about student learning and make informed decisions to improve their teaching. While much attention has been paid to developing teacher-facing dashboards, less is known about how…
Descriptors: Learning Analytics, Planning, Design, Evaluation
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Prinsloo, Paul; Slade, Sharon; Khalil, Mohammad – British Journal of Educational Technology, 2023
Since the emergence of learning analytics (LA) in 2011 as a distinct field of research and practice, multimodal learning analytics (MMLA), shares an interdisciplinary approach to research and practice with LA in its use of technology (eg, low cost sensors, wearable technologies), the use of artificial intelligence (AI) and machine learning (ML),…
Descriptors: Multimedia Instruction, Learning Analytics, Privacy, Student Rights
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Thompson, Terrie Lynn; Prinsloo, Paul – Learning, Media and Technology, 2023
Learning analytics offer centralization of a particular understanding of learning, teaching, and student support alongside data-informed insight and foresight. As such, student-related data in higher education can be imagined and enacted as a 'data frontier' in which the data gaze is expanding, intensifying, and performing new meanings and…
Descriptors: Learning Analytics, Data, Activism, Higher Education
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Prinsloo, Paul; Kaliisa, Rogers – British Journal of Educational Technology, 2022
Whilst learning analytics is still nascent in most African higher education institutions, many African higher education institutions use learning platforms and analytic services from providers "outside" of the African continent. A critical consideration of the protection of data privacy on the African continent and its implications for…
Descriptors: Foreign Countries, Information Security, Privacy, Data
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Slade, Sharon; Prinsloo, Paul; Khalil, Mohammad – Information and Learning Sciences, 2023
Purpose: The purpose of this paper is to explore and establish the contours of trust in learning analytics and to establish steps that institutions might take to address the "trust deficit" in learning analytics. Design/methodology/approach: "Trust" has always been part and parcel of learning analytics research and practice,…
Descriptors: Trust (Psychology), Learning Analytics, Privacy, Artificial Intelligence
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Prinsloo, Paul; Slade, Sharon; Khalil, Mohammad – British Journal of Educational Technology, 2022
Evidence shows that appropriate use of technology in education has the potential to increase the effectiveness of, eg, teaching, learning and student support. There is also evidence that technology can introduce new problems and ethical issues, e.g., student privacy. This article maps some limitations of technological approaches that ensure…
Descriptors: Student Records, Data, Privacy, Learning Analytics
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Prinsloo, Paul; Kaliisa, Rogers – Journal of Learning Analytics, 2022
While learning analytics (LA) has been highlighted as a field aiming to address systemic equity and quality issues within educational systems between and within regions, to date, its adoption is predominantly in the Global North. Since the Society for Learning Analytics Research (SoLAR) aspires to be international in reach and relevance, and to…
Descriptors: Learning Analytics, Equal Education, Educational Quality, Diversity
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Prinsloo, Paul; Slade, Sharon; Khalil, Mohammad – Journal of Research on Technology in Education, 2023
This article seeks to explore different combinations of human and Artificial Intelligence (AI) decision-making in the context of distributed learning. Distributed learning institutions face specific challenges such as high levels of student attrition and ensuring quality, cost-effective student support at scale using a range of technologies, such…
Descriptors: Decision Making, Algorithms, Artificial Intelligence, Cost Effectiveness
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Prinsloo, Paul – British Journal of Educational Technology, 2019
Data--their collection, analysis and use--have always been part of education, used to inform policy, strategy, operations, resource allocation, and, in the past, teaching and learning. Recently, with the emergence of learning analytics, the collection, measurement, analysis and use of student data have become an increasingly important research…
Descriptors: Learning Analytics, Data Collection, Data Analysis, Measurement
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Broughan, Christine; Prinsloo, Paul – Assessment & Evaluation in Higher Education, 2020
Student data, whether in the form of engagement data, assignments or examinations, form the foundation for assessment and evaluation in higher education. As higher education institutions progressively move to blended and online environments, we have access to, not only more data than before, but also a greater variety of demographic and…
Descriptors: Learning Analytics, Student Centered Learning, Student Empowerment, Data Collection
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Archer, Elizabeth; Prinsloo, Paul – Assessment & Evaluation in Higher Education, 2020
Assessment and learning analytics both collect, analyse and use student data, albeit different types of data and to some extent, for various purposes. Based on the data collected and analysed, learning analytics allow for decisions to be made not only with regard to evaluating progress in achieving learning outcomes but also evaluative judgments…
Descriptors: Learning Analytics, Student Evaluation, Educational Objectives, Student Behavior